In modern medical practices physicians often dictate medical reports into a telephone transcription center. As a physician dictates a report via telephone into the transcription center the report is recorded into an audio file, typically in digital format, and stored into a database. The transcription center will stop recording audio when it receives a hang up signal from the telephone system. The recorded audio file is then accessed either by a manual transcriptionist for transcribing, or more recently, the audio file is processed by a speech recognition engine. In the latter case, the speech recognition engine processes the audio into text and prepares a written report of the original dictated report.
It has been found that the transcription center will sometimes continue to record audio even after the physician has completed the dictation and hung up the telephone. In these cases the transcription center does not recognize a hang up signal from the telephone system or the telephone fails to immediately send a hang up signal and close the telephone line. This results in audio files recorded at the transcription center that often include anomaly portions, e.g. include hang up sounds, busy signals, fast busy signals and extended superfluous “dead” air portions. It is not uncommon for audio files to contain a limited amount of actual valuable audio, i.e., medical dictation, and a substantial portion of anomaly signals. In some cases it has been found that the anomaly portion of any particular audio file can be up to and even more than ten minutes in length.
Audio files containing anomalies can waste valuable database, transcriptionist and overall transcription system resources. Further, such audio files have been known to crash speech recognition engines as well as providing poor samples for acoustic signatures for a particular physician during speech recognition engine training.
Various systems have been implemented to solve this problem. For example there are systems which detect of a busy signals, however such system tend to run to run Fast Fourier Transform (“FFT”) or some of kind of signal detection, it might be a little slow to do that or you need special hardware to do that. It is desirable to provide a system that performs the searching and detection of anomalies in sound recordings in fast and efficient manner and without the need for additional expensive software or hardware.